Research of the Prediction of Stock Market Price Trends Based on Several Models

The property market is an important part of the world’s money system, and being able to determine how stock prices may walk is essential for making wise investment choices. Because stock markets can change quickly and are complicated, traditional methods often do not give accurate predictions. This research looks at innovative ways to determine stock marketplace price trends. It focuses on strategies like AutoRegressive Integrated Moving Average (ARI-MA), Random Trees, and Long Short-Word Storage (LSTM) neural networks. Using earlier inventory value data, we checked how well these concepts worked by looking at accuracy methods like Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). This helped us discover how great they were at understanding the complex patterns in stock prices. The findings show that LSTM, a type of deep learning model, works better than traditional quantitative methods. It is great at understanding extended-term connections and dealing with difficult relationships in unpredictable markets. Machine learning designs like Random Forests are great at recognizing quick-term trends, but ARIMA works well for data that is stable or follows a design over time. However, there are still issues like changing market conditions, the risk of concepts fitting too tightly to previous information, and the need for excel-lent quality information in all methods. The research shows that using various methods can increase the accuracy of estimating stock price changes. This helps traders make better decisions in altering market conditions.